Segmentation of Hyper-Acute Ischemic Infarcts from Diffusion Weighted Imaging Based on Support Vector Machine

DOI: 10.4236/jcc.2015.311024   PDF   HTML   XML   2,853 Downloads   3,285 Views   Citations

Abstract

Accurate and automatic segmentation of hyper-acute ischemic infarct from magnetic resonance imaging is of great importance in clinical trials. Manual delineation is labor intensive, exhibits great variability due to unclear infarct boundaries, and most importantly, is not practical due to urgent time requirement for prompt therapy. In this paper, segmentation of hyper-acute ischemic infarcts from diffusion weighted imaging based on Support Vector Machine (SVM) is explored. Experiments showed that SVM could provide good agreement with manual delineations by experienced experts to achieve an average Dice coefficient of 0.7630.121. The proposed method could achieve significantly higher segmentation accuracy and could be a potential tool to assist clinicians for quantifying hyper-acute infarction and decision making especially for thrombolytic therapy.

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Peng, Y. , Zhang, X. and Hu, Q. (2015) Segmentation of Hyper-Acute Ischemic Infarcts from Diffusion Weighted Imaging Based on Support Vector Machine. Journal of Computer and Communications, 3, 152-157. doi: 10.4236/jcc.2015.311024.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Du Plooy, J.N., Buys, A., Duim, W. and Pretorius, E. (2013) Comparison of Platelet Ultrastructure and Elastic Properties in Thrombo-Embolic Ischemic Stroke and Smoking Using Atomic Force and Scanning Electron Microscopy. Plos One, 8. http://dx.doi.org/10.1371/journal.pone.0069774
[2] Muir, K.W., Buchan, A., von Kummer, R., Rother, J. and Baron, J.C. (2006) Imaging of Acute Stroke. Lancet Neurol, 5, 755-768. http://dx.doi.org/10.1016/S1474-4422(06)70545-2
[3] Prakash, K.N.B., Gupta, V., Hu, J.B. and Nowinski, W.L. (2008) Automatic Processing of Diffusion-Weighted Ischemic Stroke Images Based on Divergence Measures: Slice and Hemisphere Identification, and Stroke Region Segmentation. Int J Comput Ass Rad, 3, 559-570.
[4] Tsai, J.Z., Peng, S.J., Chen, Y.W., Wang, K.W., Wu, H.K., Lin, Y.Y., Lee, Y.Y., Chen, C.J., Lin, H.J., Smith, E.E., Yeh, P.S. and Hsin, Y.L. (2014) Automatic Detection and Quantification of Acute Cerebral Infarct by Fuzzy Clustering and Histographic Characterization on Diffusion Weighted Mr Imaging and Apparent Diffusion Coefficient Map. Biomed Res Int, 13. http://dx.doi.org/10.1155/2014/963032
[5] Ma, L., Gao, P.Y., Hu, Q.M., Lin, Y., Jing, L.N., Xue, J., Wang, X.C., Chen, Z.J., Wang, Y.L., Liao, X.L., Liu, M.L. and Chen, W.J. (2010) Prediction of Infarct Core and Salvageable Ischemic Tissue Volumes by Analyzing Apparent Diffusion Coefficient without Intravenous Contrast Material. Acad Radiol, 17, 1506-1517. http://dx.doi.org/10.1016/j.acra.2010.07.010
[6] Hu, Q.M. and Nowinski, W.L. (2003) A Rapid Algorithm for Robust and Automatic Extraction of the Midsagittal Plane of the Human Cerebrum from Neuroimages Based on Local Symmetry and Outlier Removal. NeuroImage, 20, 2153-2165. http://dx.doi.org/10.1016/j.neuroimage.2003.08.009
[7] Cortes, C. and Vapnik, V. (1995) Support-Vector Networks. Mach. Learn., 20, 273-297. http://dx.doi.org/10.1007/BF00994018
[8] Chang, C.C. and Lin, C.J. (2011) LIBSVM: A Library for Support Vector Machines. Acm T Intel Syst Tec, 2.
[9] Cao, L., Li, J., Sun, Y., Zhu, H. and Yan, C. (2010) EEG-Based Vigilance Analysis by Using Fisher Score and PCA Algorithm. 2010 IEEE International Conference on Progress in Informatics and Computing (PIC), 175-179.
[10] Zhu, W. and Lin, Y. (2013) Using Gini-Index for Feature Weighting in Text Ca-tegorization. J Comput Inf Sys, 9, 5819- 5826.
[11] Tourassi, G.D., Frederick, E.D., Markey, M.K. and Floyd, C.E. (2001) Application of the Mutual Information Criterion for Feature Selection in Computer-Aided Diagnosis. Med Phys, 28, 2394-2402. http://dx.doi.org/10.1118/1.1418724
[12] Peng, H., Long, F. and Ding, C. (2005) Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Trans Pattern Anal Mach Intell, 27, 1226-1238. http://dx.doi.org/10.1109/TPAMI.2005.159
[13] Robnik-Sikonja, M. and Kononenko, I. (2003) Theoretical and Em-pirical Analysis of ReliefF and RReliefF. Mach. Learn., 53, 23-69. http://dx.doi.org/10.1023/A:1025667309714

  
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